One of the greatest challenges in achieving adequate scaling of resources in a working environment such as a call center (e.g., performing tasks, assisting customers, responding to service inquiries and the like) is scheduling a sufficient number of support staff when those individuals are most needed (i.e., when demand for service is high), but without having to incur extraordinary expenses.
Generally, a common method is to assign a fixed schedule of working hours to 100% of the population of workers. Typically, this method results in many staff members being displeased with their work schedule at some point during the schedule period because the schedule is inflexible but the workers' schedules have unforeseen changes (e.g., sickness, appointments, emergencies). Another method is to put a work schedule out to bid, often giving more senior staff members first priority to choose working hours. Typically, this method results in having certain staff members assigned to work during time periods where their skillset and experience may not match the service needs at that time. Another method is the distributed model, where all staff members work from home. However, this method in fact does not address flexible scheduling or peak service needs at all. Instead, it is typically just a ploy to save money on office space regardless of meeting the staff member needs or employer requirements. As an example, FIG. 1 is a chart comparing customer request volume for a center with staff member scheduling. As shown in FIG. 1, the customer request volume 102 typically does not synchronize with the availability of staff members 104 to handle the requests. The gaps 106 between the two curves represent the times of day when the incoming service requests are not covered by the available staff members—indicating a deficiency of service.
And, none of the methods described above allow for intra-period schedule changes (e.g., a snowstorm near an office that causes power outages or disrupts the work environment, a staff member has a sick infant and unexpectedly needs to visit the doctor, and so forth). For example, in the latter scenario, the staff member would likely miss an entire day of work instead of working less than a full day, going to the appointment at 9:00 am, and then returning to work between 11:00 am and 3:00 pm, which coincide with peak staffing needs at the workplace.
In addition, the above scheduling methods do not leverage advanced data aggregation and computing technologies relating to the staff members, such as information gleaned from mobile devices and wearable devices assigned to the staff members, in order to dynamically call in additional support staff (also called ‘just-in-time’ staffing) in times of increased need or unexpected schedule changes and ensure that the workplace is adequately staffed at all times.